DocumentCode
604383
Title
PSO-RBFNN based optimized PNN classifier model
Author
Jin Liu ; Xiao Fu ; Xingbin Yao
Author_Institution
Dept. of Fundamental Courses, Air Force Aviation Univ., Changchun, China
fYear
2012
fDate
29-31 Dec. 2012
Firstpage
456
Lastpage
459
Abstract
In this paper, a self-adaptive method of iris boundary detection is presented and the method can segment the iris area accurately regardless of the shapes of iris boundaries. On the same time, a new feature extraction technique based on combination using special Gabor filters and wavelet maxima components is proposed. Finally, The radial basis function neural network (RBFNN) with a particle swarm optimization (PSO) a novel iris iris recognition technique with intelligent classifier is proposed for high performance iris recognition. this paper combines radial basis function neural network (RBFNN) and particle swarm optimization (PSO) for an optimized PNN classifier model. The experimental results reveal the proposed algorithm provides superior performance in iris recognition.
Keywords
Gabor filters; edge detection; image classification; image segmentation; iris recognition; particle swarm optimisation; radial basis function networks; wavelet transforms; Gabor filters; PNN classifier model; PSO-RBFNN; feature extraction technique; intelligent classifier; iris area segmentation; iris boundary detection; iris recognition technique; particle swarm optimization; probabilistic neural network; radial basis function neural network; self-adaptive method; wavelet maxima components; Particle swarm optimization; Probabilistic neural network; RBF neural networks; iris recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference on
Conference_Location
Changchun
Print_ISBN
978-1-4673-2963-7
Type
conf
DOI
10.1109/ICCSNT.2012.6525976
Filename
6525976
Link To Document